Automatic T Staging Using Weakly Supervised Deep Learning for Nasopharyngeal Carcinoma on MR Images.
deep learning
magnetic resonance imaging
nasopharyngeal carcinoma
staging
Journal
Journal of magnetic resonance imaging : JMRI
ISSN: 1522-2586
Titre abrégé: J Magn Reson Imaging
Pays: United States
ID NLM: 9105850
Informations de publication
Date de publication:
10 2020
10 2020
Historique:
received:
18
02
2020
revised:
07
05
2020
accepted:
07
05
2020
pubmed:
26
6
2020
medline:
15
5
2021
entrez:
26
6
2020
Statut:
ppublish
Résumé
Recent studies have shown that deep learning can help tumor staging automatically. However, automatic nasopharyngeal carcinoma (NPC) staging is difficult due to the lack of large and slice-level annotated datasets. To develop a weakly-supervised deep-learning method to predict NPC patients' T stage without additional annotations. Retrospective. In all, 1138 cases with NPC from 2010 to 2012 were enrolled, including a training set (n = 712) and a validation set (n = 426). 1.5T, T We used a weakly-supervised deep-learning network to achieve automated T staging of NPC. T usually refers to the size and extent of the main tumor. The training set was employed to construct the deep-learning model. The performance of the automated T staging model was evaluated in the validation set. The accuracy of the model was assessed by the receiver operating characteristic (ROC) curve. To further assess the performance of the deep-learning-based T score, the progression-free survival (PFS) and overall survival (OS) were performed. The Sklearn package in Python was applied to calculate the area under the curve (AUC) of the ROC. The survcomp package was used for calculations and comparisons between C-indexes. The software SPSS was employed to conduct survival analysis and chi-square tests. The accuracy of the deep-learning model was 75.59% in the validation set. The average AUC of the ROC curve of different stages was 0.943. There were no significant differences in the C-indexes of PFS and OS from the deep-learning model and those from TNM staging, with P values of 0.301 and 0.425, respectively. This weakly-supervised deep-learning approach can perform fully automated T staging of NPC and achieve good prognostic performance. 3 Technical Efficacy Stage: 2 J. Magn. Reson. Imaging 2020;52:1074-1082.
Sections du résumé
BACKGROUND
Recent studies have shown that deep learning can help tumor staging automatically. However, automatic nasopharyngeal carcinoma (NPC) staging is difficult due to the lack of large and slice-level annotated datasets.
PURPOSE
To develop a weakly-supervised deep-learning method to predict NPC patients' T stage without additional annotations.
STUDY TYPE
Retrospective.
POPULATION/SUBJECTS
In all, 1138 cases with NPC from 2010 to 2012 were enrolled, including a training set (n = 712) and a validation set (n = 426).
FIELD STRENGTH/SEQUENCE
1.5T, T
ASSESSMENT
We used a weakly-supervised deep-learning network to achieve automated T staging of NPC. T usually refers to the size and extent of the main tumor. The training set was employed to construct the deep-learning model. The performance of the automated T staging model was evaluated in the validation set. The accuracy of the model was assessed by the receiver operating characteristic (ROC) curve. To further assess the performance of the deep-learning-based T score, the progression-free survival (PFS) and overall survival (OS) were performed.
STATISTICAL TESTS
The Sklearn package in Python was applied to calculate the area under the curve (AUC) of the ROC. The survcomp package was used for calculations and comparisons between C-indexes. The software SPSS was employed to conduct survival analysis and chi-square tests.
RESULTS
The accuracy of the deep-learning model was 75.59% in the validation set. The average AUC of the ROC curve of different stages was 0.943. There were no significant differences in the C-indexes of PFS and OS from the deep-learning model and those from TNM staging, with P values of 0.301 and 0.425, respectively.
DATA CONCLUSION
This weakly-supervised deep-learning approach can perform fully automated T staging of NPC and achieve good prognostic performance.
LEVEL OF EVIDENCE
3 Technical Efficacy Stage: 2 J. Magn. Reson. Imaging 2020;52:1074-1082.
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
1074-1082Informations de copyright
© 2020 International Society for Magnetic Resonance in Medicine.
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